Vibrational Force on Accelerating Orthodontic Tooth Movement: A Systematic Review and Meta-Analysis

This study aimed to systematically gather and analyze the current level of evidence for the effectiveness of the vibrational force in accelerating orthodontic tooth movement (OTM). This systematic review was conducted using three electronic databases: Scopus, PubMed, and Google Scholar until March 2022. The search was done through the following journals: European Journal of Orthodontics, American Journal of Orthodontics and Dentofacial Orthopedics, The Angle Orthodontist, Progress in Orthodontics, and Seminars in Orthodontics. Human or animal studies that have evaluated the effect of vibrational force on the rate of OTM were selected. A meta-analysis was performed for the rate of canine movement per month. Database research, elimination of duplicate studies, data extraction, and risk of bias assessment were performed by authors independently and in duplication. A fixed and random-effect meta-analysis was performed to evaluate the effect of vibrational forces. A total of 19 studies (6 animal and 13 human studies) that met the inclusion criteria were included. Meta-analysis was performed based on four human clinical trials. Three out of four studies showed no significant difference in the rate of canine movement between vibrational force and control groups. The limitation of this study was the small sample size and significant heterogeneity among the studies. Although vibrational forces have been shown to accelerate OTM in experimental studies, the results are inconsistent in clinical studies. The inability to apply desired peak load to the targeted teeth may be the main factor in inconsistent clinical outcomes

Requirements for gene silencing mediated by U1 snRNA binding to a target sequence

U1 interference (U1i) is a novel method to block gene expression. U1i requires expression of a 5′-end-mutated U1 snRNA designed to base pair to the 3′-terminal exon of the target gene's pre-mRNA that leads to inhibition of polyadenylation. Here, we show U1i is robust (≥95%) and a 10-nt target length is sufficient for good silencing. Surprisingly, longer U1 snRNAs, which could increase annealing to the target, fail to improve silencing. Extensive mutagenesis of the 10-bp U1 snRNA:target duplex shows that any single mismatch different from GU at positions 3–8, destroys silencing. However, mismatches within the other positions give partial silencing, suggesting that off-target inhibition could occur. The specificity of U1i may be enhanced, however, by the fact that silencing is impaired by RNA secondary structure or by splicing factors binding nearby, the latter mediated by Arginine-Serine (RS) domains. U1i inhibition can be reconstituted in vivo by tethering of RS domains of U1-70K and U2AF65. These results help to: (i) define good target sites for U1i; (ii) identify and understand natural cellular examples of U1i; (iii) clarify the contribution of hydrogen bonding to U1i and to U1 snRNP binding to 5′ splice sites and (iv) understand the mechanism of U1i

Ophthalmology

OBJECTIVE: In the current study we aimed to identify metabolites associated with age-related macular degeneration (AMD) by performing the largest metabolome association analysis in AMD to date. In addition, we aimed to determine the effect of AMD-associated genetic variants on metabolite levels, and aimed to investigate associations between the identified metabolites and activity of the complement system, one of the main AMD-associated disease pathways. DESIGN: Case-control assocation analysis of metabolomics data. SUBJECTS: 2,267 AMD cases and 4,266 controls from five European cohorts. METHODS: Metabolomics was performed using a high-throughput H-NMR metabolomics platform, which allows the quantification of 146 metabolite measurements and 79 derivative values. Metabolome-AMD associations were studied using univariate logistic regression analyses. The effect of 52 AMD-associated genetic variants on the identified metabolites was investigated using linear regression. In addition, associations between the identified metabolites and activity of the complement pathway (defined by the C3d/C3 ratio) were investigated using linear regression. MAIN OUTCOME MEASURES: Metabolites associated with AMD RESULTS: We identified 60 metabolites that were significantly associated with AMD, including increased levels of large and extra-large HDL subclasses and decreased levels of VLDL, amino acids and citrate. Out of 52 AMD-associated genetic variants, seven variants were significantly associated with 34 of the identified metabolites. The strongest associations were identified for genetic variants located in or near genes involved in lipid metabolism (ABCA1, CETP, APOE, LIPC) with metabolites belonging to the large and extra-large HDL subclasses. In addition, 57 out of 60 metabolites were significantly associated with complement activation levels, and these associations were independent of AMD status. Increased large and extra-large HDL levels and decreased VLDL and amino acid levels were associated with increased complement activation. CONCLUSIONS: Lipoprotein levels were associated with AMD-associated genetic variants, while decreased essential amino acids may point to nutritional deficiencies in AMD. We observed strong associations between the vast majority of the AMD-associated metabolites and systemic complement activation levels, independent of AMD status. This may indicate biological interactions between the main AMD disease pathways, and suggests that multiple pathways may need to be targeted simultaneously for successful treatment of AMD

Updates in Grading of Renal Cell Carcinomas Beyond Clear Cell Renal Cell Carcinoma and Papillary Renal Cell Carcinoma

The World Health Organization (WHO) recommends grading of clear cell renal cell carcinoma (RCC) and papillary RCC using the WHO/International Society of Urological Pathology (ISUP) grade, which is primarily based on nuclear features. As the spectrum of RCC continues to evolve, with more recently described subtypes in the past decade, literature evidence on grading these subtypes is limited or not available for some tumor types. Herein, we outline a pragmatic approach to the topic of grading RCC, dividing the contemporarily described RCC subtypes into 7 categories based on the potential clinical applicability of grading as a useful prognostic parameter: (1) RCC subtypes that are reasonably validated and recommended for WHO/ISUP grading; (2) RCC subtypes where WHO/ISUP is not applicable; (3) RCC subtypes where WHO/ISUP grading is potentially clinically useful; (4) inherently aggressive RCC subtypes where histologic classification itself confers an aggressive biologic potential; (5) renal epithelial tumors where WHO/ISUP grading provides potentially misleading prognostic implication; (6) renal epithelial neoplasms where low WHO/ISUP grade features are a prerequisite for accurate histologic classification; and (7) renal epithelial neoplasms with no or limited data on grading or incomplete understanding of the biologic potential. Our aim in outlining this approach is 2-fold: (a) identify the gaps in understanding and application of grading in RCC subtypes so that researchers in the field may perform additional studies on the basis of which the important pathologic function of assignment of grade may be recommended to be performed as a meaningful exercise across a wider spectrum of RCC; and (b) to provide guidance in the interim to surgical pathologists in terms of providing clinically useful grading information in RCC based on currently available clinicopathologic information

Impact of T Cell Dose on Outcome of T Cell-Replete HLA-Matched Allogeneic Peripheral Blood Stem Cell Transplantation

Data on whether the T cell dose of allogeneic peripheral blood stem cell (PBSC) products influences transplantation outcomes are conflicting. Using the Center for International Blood and Marrow Transplant Research database, we identified 2736 adult patients who underwent first allogeneic PBSC transplantation for acute leukemia or myelodysplastic syndrome between 2008 and 2014 using an HLA-matched sibling donor (MSD) or an 8/8-matched unrelated donor (MUD). We excluded ex vivo and in vivo T cell-depleted transplantations. Correlative analysis was performed between CD3 T cell dose and the risk of graft-versus-host-disease (GVHD), relapse, nonrelapse mortality (NRM), disease-free survival (DFS), and overall survival (OS). Using maximum likelihood estimation, we identified CD3 T cell dose cutoff that separated the risk of acute GVHD (aGVHD) grade II-IV in both the MSD and MUD groups. A CD3 T cell dose cutoff of 14 × 10 cells/kg identified MSD/low CD3 (n = 223) and MSD/high CD3 (n = 1214), and a dose of 15 × 10 cells/kg identified MUD/low CD3 (n = 197) and MUD/high CD3 (n = 1102). On univariate analysis, the MSD/high CD3 group had a higher cumulative incidence of day +100 aGVHD grade II-IV compared with the MSD/low CD3 group (33% versus 25%; P =.009). There were no differences between the 2 groups in engraftment rate, risk of aGVHD grade III-IV or chronic GVHD (cGVHD), NRM, relapse, DFS, or OS. The MUD/high CD3 group had a higher cumulative incidence of day +100 aGVHD grade II-IV compared with the MUD/low CD3 group (49% versus 41%; P =.04). There were no differences between the 2 groups in engraftment rate, risk of severe aGVHD or cGVHD, NRM, relapse, DFS, or OS. Multivariate analysis of the MSD and MUD groups failed to show an association between CD3 T cell dose and the risk of either aGVHD grade II-IV (P =.10 and.07, respectively) or cGVHD (P =.80 and.30, respectively). Subanalysis of CD4 T cells, CD8 T cells, and CD4+/CD8+ ratio failed to identify cutoff values predictive of transplantation outcomes; however, using the log-rank test, the sample size was suboptimal for identifying a difference at this cutoff cell dose. In this registry study, the CD3 T cell dose of PBSC products did not influence the risk of aGVHD or cGVHD or other transplantation outcomes when using an MSD or an 8/8-matched MUD. Subset analyses of CD4 and CD8 T cell doses were not possible given our small sample size